Zooplankton salinity relationships in Suisun
Sam Bashevkin
8/26/2022
Load packages
require(conflicted)
require(MASS)
require(dplyr)
require(zooper)
require(lubridate)
require(readr)
require(tidyr)
require(ggplot2)
require(sf)
require(readxl)
require(stringr)
require(mgcv)
require(purrr)
require(deltamapr)
require(scales)
conflict_prefer("filter", "dplyr")
conflict_prefer("select", "dplyr")Load and wrangle data
zoop_data<-Zoopsynther(Data_type="Community", Sources=c("EMP", "STN", "20mm", "FMWT"), Time_consistency = TRUE)## [1] "These species have no relatives in their size class common to all datasets and have been removed from one or more size classes: Ostracoda Adult (Meso), Cumacea Adult (Meso), Annelida Adult (Meso), Gammarus Adult (Meso), Orientomysis aspera Adult (Meso), Chironomidae Larva (Meso), Insecta Larva (Meso)"Read in zoop mass conversions
zoop_mass_conversions<-read_excel("Data/SMSCG salinity modeling/Biomass conversions.xlsx", sheet="Micro and Meso-zooplankton")%>%
mutate(Taxname=case_when(Taxname=="Sinocalanus"~"Sinocalanus doerrii", # Change to help this match to zoop data
TRUE ~ Taxname),
Taxlifestage=paste(Taxname, Lifestage))%>%
select(Taxlifestage, CarbonWeight_ug)Read in zoop groupings
zoop_groups<-read_csv("Data/zoopcrosswalk2.csv", col_types=cols_only(Taxlifestage="c", IBMR="c"))%>%
distinct()Load Mysid biomass data
zoop_mysid<-read_excel("Data/1972-2020MysidBPUEMatrix.xlsx", # EMP
sheet="Mysid_BPUE_matrix_1972-2020", na = "NA",
col_types = c(rep("numeric", 4), "date", "text", "text", rep("text", 7), rep("numeric", 8)))%>%
select(Date=SampleDate, Station=StationNZ, BPUE=`Hyperacanthomysis longirostris`)%>% # Only select Hyperacanthomysis longirostris
mutate(Source="EMP")%>%
bind_rows(read_csv("Data/FMWT STN 2007to2019 Mysid BPUE.csv", # FMWT/STN
col_types=cols_only(Station="c", SampleDate="c", Project="c", `Hyperacanthomysis longirostris`="d"))%>%
rename(Date=SampleDate, Source=Project, BPUE=`Hyperacanthomysis longirostris`)%>% # Only select Hyperacanthomysis longirostris
mutate(Date=mdy(Date),
Station=recode(Station, MONT="Mont", HONK="Honk")))%>% #Get station names to match to main dataset
mutate(BPUE_mysid=BPUE*1000, # Convert to ug
Taxlifestage="Hyperacanthomysis longirostris Adult",
SampleID=paste(Source, Station, Date),
SizeClass="Macro")%>%
select(SampleID, Taxlifestage, SizeClass, BPUE_mysid)Start processing the zoop data
zoop_data_mass<-zoop_data%>%
mutate(Taxlifestage=str_remove(Taxlifestage, fixed("_UnID")))%>%
filter(
!(SizeClass=="Meso" & #eliminating species which are counted in meso and micro and retained better in the micro net from the meso calcs
Taxlifestage%in%c("Asplanchna Adult", "Copepoda Larva","Cyclopoida Juvenile", "Eurytemora Larva", "Harpacticoida Undifferentiated",
"Keratella Adult", "Limnoithona Adult", "Limnoithona Juvenile", "Limnoithona sinenesis Adult", "Limnoithona tetraspina
Adult", "Oithona Adult", "Oithona Juvenile", "Oithona davisae Adult", "Polyarthra Adult","Pseudodiaptomus Larva",
"Rotifera Adult", "Sinocalanus doerrii Larva", "Synchaeta Adult", "Synchaeta bicornis Adult", "Trichocerca Adult")) &
!(SizeClass=="Micro" &Taxlifestage%in%c("Cirripedia Larva", "Cyclopoida Adult", "Oithona similis")) & #removing categories better retained in meso net from micro net matrix
Order!="Amphipoda" & # Remove amphipods
(Order!="Mysida" | Taxlifestage=="Hyperacanthomysis longirostris Adult"))%>% #Only retain Hyperacanthomysis longirostris
mutate(Taxlifestage=recode(Taxlifestage, `Synchaeta bicornis Adult`="Synchaeta Adult", # Change some names to match to biomass conversion dataset
`Pseudodiaptomus Adult`="Pseudodiaptomus forbesi Adult",
`Acanthocyclops vernalis Adult`="Acanthocyclops Adult"))%>%
left_join(zoop_mass_conversions, by="Taxlifestage")%>% # Add biomass conversions
left_join(zoop_mysid, by=c("SampleID", "Taxlifestage", "SizeClass"))%>% # Add mysid biomass
left_join(zoop_groups, by="Taxlifestage")%>% # Add IBMR categories
mutate(BPUE=if_else(Taxlifestage=="Hyperacanthomysis longirostris Adult", BPUE_mysid, CPUE*CarbonWeight_ug))%>% # Create 1 BPUE variable
filter(!is.na(BPUE) & !is.na(Latitude) & !is.na(Longitude) & !is.na(SalSurf))%>% # Removes any data without BPUE, which is currently restricted to Decapod Larvae, and H. longirostris from STN. Also removes 20mm and EMP EZ stations without coordinates
group_by(IBMR)%>%
mutate(flag=if_else(all(c("Micro", "Meso")%in%SizeClass), "Remove", "Keep"))%>% # This and the next 2 lines are meant to ensure that all categories are consistent across the surveys. Since only EMP samples microzoops, only EMP data can be used for categories that include both micro and mesozoops.
ungroup()%>%
filter(!(flag=="Remove" & Source!="EMP"))%>%
select(SampleID, Station, Latitude, Longitude, SalSurf, Date, Year, IBMR, BPUE)%>%
group_by(across(-BPUE))%>%
summarise(BPUE=sum(BPUE), .groups="drop")%>% # Sum each IBMR categories
st_as_sf(coords=c("Longitude", "Latitude"), crs=4326)%>%
st_transform(crs=st_crs(deltamapr::R_DSIBM)) %>%
st_join(deltamapr::R_DSIBM %>%
select(SUBREGION)) %>%
st_drop_geometry() %>%
filter(SUBREGION %in% c("NW Suisun","SW Suisun","NE Suisun","SE Suisun","Confluence", "Suisun Marsh"))%>%
mutate(doy=yday(Date), #Day of year
Month=month(Date), # Month
Year_fac=factor(Year), # Factor year for model random effect
Station_fac=factor(Station), # Factor station for model random effect
across(c(SalSurf, doy), list(s=~(.x-mean(.x))/sd(.x))), # Center and standardize predictors
BPUE_log1p=log(BPUE+1)) # log1p transform BPUE for modelCheck sample size
zoop_sample_size <- zoop_data_mass %>%
group_by(SampleID,Year,Month,SUBREGION,Station) %>%
summarise(BPUE=sum(BPUE)) %>%
mutate(Samplesize=1) %>%
group_by(Year, Month, SUBREGION) %>%
summarise(mean_BPUE=mean(BPUE),Samplesize=sum(Samplesize)) %>%
filter(Year>=1995)
ggplot(zoop_sample_size, aes(x=Year, y=Month, fill=Samplesize))+
geom_tile()+
scale_y_continuous(breaks=1:12, labels=month(1:12, label=T))+
scale_fill_viridis_c(breaks=c(1,5,10,15,20))+
facet_wrap(~SUBREGION)+
theme_bw()
All the remaining brackish regions have sufficient sample size with the exception of NE Suisun. As such, NE Suisun is to be combined with SE Suisun while the rest of the regions are to be analyzed on their own.
Create a new column with IBMR edited regions to accomodate combination of NE and SE Suisun regions.
zoop_data_mass$Subregion_edit<-ifelse(zoop_data_mass$SUBREGION%in%c("NE Suisun", "SE Suisun"), "East Suisun", zoop_data_mass$SUBREGION)Model
Prediction data
Set up prediction data for model
# Min year to start models
year_min<-1995
newdata_function<-function(region, data=zoop_data_mass, quant=0.99){
lower<-(1-quant)/(2)
upper<-1-lower
data_filt<-data%>%
filter(Subregion_edit%in%region & Year >= year_min)
# Calculate monthly quantiles of salinity
month_sal<-data_filt%>%
group_by(Month)%>%
summarise(l=quantile(SalSurf, lower),
u=quantile(SalSurf, upper), .groups="drop")
newdata<-expand_grid(date=mdy(paste(1:12, 15, 2001, sep="/")), # The 15th of each month on a non-leap year
SalSurf=seq(round(min(data_filt$SalSurf), 1),
round(max(data_filt$SalSurf), 1), by=0.1))%>% # Salinity sequence nicely rounded to 1 decimal
mutate(Month=month(date),
doy=yday(date), # Day of year
SalSurf_s=(SalSurf-mean(data$SalSurf))/sd(data$SalSurf), # center and standardize salinity to match data
doy_s=(doy-mean(data$doy))/sd(data$doy))%>% # center and standardize doy to match data
left_join(month_sal, by="Month")%>%
filter(SalSurf >= l & SalSurf <= u)%>% # Remove any salinity values outside the quantiles for each month
select(Month, doy, doy_s, SalSurf, SalSurf_s)
}
newdata<-map(set_names(unique(zoop_data_mass$Subregion_edit)), newdata_function)Posterior prediction function
# Function to generate posterior predictions from a gam model
# From https://stats.stackexchange.com/questions/190348/can-i-use-bootstrapping-to-estimate-the-uncertainty-in-a-maximum-value-of-a-gam
predict_posterior<-function(model, newdata, exclude, n=1e3, seed=999){
Xp <- predict(model, newdata=newdata, type="lpmatrix", exclude=exclude, newdata.guaranteed=TRUE) ## map coefs to fitted curves
beta <- coef(model)
Vb <- vcov(model) ## posterior mean and cov of coefs
set.seed(seed)
mrand <- mvrnorm(n, beta, Vb) ## simulate n rep coef vectors from posterior
pred<-matrix(nrow=nrow(newdata), ncol=n)
ilink <- family(model)$linkinv
for (i in seq_len(n)) {
pred[,i] <- ilink(Xp %*% mrand[i, ])
}
colnames(pred)<-paste("draw", 1:n, sep="_")
pred<-as_tibble(pred)
return(pred)
}Model fitting
model
sal_model<-function(group,region,new_data=newdata){
cat("<<<<<<<<<<<<<<<<<<<<<<< modeling", group, region, ">>>>>>>>>>>>>>>>>>>>>>>>>\n\n")
new_data<-new_data[[region]]
data<-filter(zoop_data_mass, IBMR==group & Subregion_edit==region & Year>=year_min)
par(mfrow=c(2,2))
if(length(unique(data$Station_fac))>1){
model<-gam(BPUE_log1p ~ te(SalSurf_s, doy_s, k=c(5,5), bs=c("cs", "cc")) +
s(Year_fac, bs="re") + s(Station_fac, bs="re"),
data=data,
method="REML")
random_effects<-c("s(Year_fac)", "s(Station_fac)")
}else{
model<-gam(BPUE_log1p ~ te(SalSurf_s, doy_s, k=c(5,5), bs=c("cs", "cc")) +
s(Year_fac, bs="re"),
data=data,
method="REML")
random_effects<-c("s(Year_fac)")
}
cat("-------------gam check-------------\n")
gam.check(model)
cat("\n\n-------------summary-------------\n")
print(summary(model))
sal<-predict_posterior(model, new_data, random_effects)%>%
bind_cols(new_data%>% # Add covariate columns before these columns
select(-doy_s, -SalSurf_s),
.)
return(sal)
}Apply model to all groups and regions
model_factors<-expand_grid(IBMR=unique(zoop_data_mass$IBMR),
Subregion_edit=unique(zoop_data_mass$Subregion_edit))%>%
mutate(IBMR=set_names(IBMR, paste(IBMR, Subregion_edit)))
sal_conversions<-pmap_dfr(model_factors, function(IBMR, Subregion_edit) sal_model(IBMR, Subregion_edit), .id = "IBMR_region")%>%
mutate(IBMR=sapply(IBMR_region, function(x) str_split(x, " ", n=2)[[1]][1]),
Region=factor(sapply(IBMR_region, function(x) str_split(x, " ", n=2)[[1]][2]),
levels=c("Confluence", "Suisun Marsh", "East Suisun",
"NW Suisun", "SW Suisun")),
Month=as.integer(Month))%>%
select(-IBMR_region, -doy)%>%
relocate(Region, Month, IBMR, SalSurf)## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 12 iterations.
## Gradient range [-0.0000001893865,0.0000001046584]
## (score 1648.549 & scale 2.551529).
## Hessian positive definite, eigenvalue range [0.8000125,421.8733].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 11.57 0.99 0.38
## s(Year_fac) 27.00 23.13 NA NA
## s(Station_fac) 5.00 3.64 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.8260 0.7375 3.832 0.000137 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 11.573 19 821.87 < 0.0000000000000002 ***
## s(Year_fac) 23.132 26 10.39 < 0.0000000000000002 ***
## s(Station_fac) 3.641 4 12.92 0.000000606 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.497 Deviance explained = 52%
## -REML = 1648.5 Scale est. = 2.5515 n = 844
## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.000006577054,0.000002917048]
## (score 1960.356 & scale 2.259893).
## Hessian positive definite, eigenvalue range [1.202923,515.3716].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.94 0.95 0.02 *
## s(Year_fac) 27.00 24.76 NA NA
## s(Station_fac) 5.00 3.32 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0033 0.3668 8.189 0.000000000000000813 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.94 19 179.02 <0.0000000000000002 ***
## s(Year_fac) 24.76 26 22.43 <0.0000000000000002 ***
## s(Station_fac) 3.32 4 20.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.639 Deviance explained = 65.4%
## -REML = 1960.4 Scale est. = 2.2599 n = 1031
## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.00003703607,0.00003036114]
## (score 4010.188 & scale 2.342237).
## Hessian positive definite, eigenvalue range [2.817146,1057.206].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 17.39 0.86 <0.0000000000000002 ***
## s(Year_fac) 27.00 25.52 NA NA
## s(Station_fac) 10.00 7.63 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1430 0.3518 11.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 17.388 19 870.10 <0.0000000000000002 ***
## s(Year_fac) 25.516 26 53.66 <0.0000000000000002 ***
## s(Station_fac) 7.627 9 14.46 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.707 Deviance explained = 71.4%
## -REML = 4010.2 Scale est. = 2.3422 n = 2115
## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0006679612,0.0004583773]
## (score 3709.235 & scale 1.878394).
## Hessian positive definite, eigenvalue range [2.589112,1039.703].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0 16.8 0.91 <0.0000000000000002 ***
## s(Year_fac) 27.0 25.2 NA NA
## s(Station_fac) 10.0 7.3 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0272 0.2351 17.13 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.775 19 3226.95 <0.0000000000000002 ***
## s(Year_fac) 25.248 26 36.85 <0.0000000000000002 ***
## s(Station_fac) 7.301 9 12.34 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.814 Deviance explained = 81.8%
## -REML = 3709.2 Scale est. = 1.8784 n = 2080
## <<<<<<<<<<<<<<<<<<<<<<< modeling acartela Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.000005676429,0.00000457835]
## (score 2999.928 & scale 1.763481).
## Hessian positive definite, eigenvalue range [1.925905,853.739].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.93 0.94 0.005 **
## s(Year_fac) 27.00 25.20 NA NA
## s(Station_fac) 8.00 5.36 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5393 0.2269 15.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.926 19 1531.672 <0.0000000000000002 ***
## s(Year_fac) 25.199 26 30.088 <0.0000000000000002 ***
## s(Station_fac) 5.358 7 8.568 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.797 Deviance explained = 80.3%
## -REML = 2999.9 Scale est. = 1.7635 n = 1708
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0001001109,0.00006445278]
## (score 1208.568 & scale 1.461434).
## Hessian positive definite, eigenvalue range [0.00005142813,364.751].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00000 12.83879 0.88 <0.0000000000000002 ***
## s(Year_fac) 27.00000 16.66107 NA NA
## s(Station_fac) 5.00000 0.00117 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.85349 0.07746 11.02 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 12.838789 19 69.950 < 0.0000000000000002 ***
## s(Year_fac) 16.661066 26 1.951 0.000000933 ***
## s(Station_fac) 0.001173 4 0.000 0.394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.572 Deviance explained = 58.9%
## -REML = 1208.6 Scale est. = 1.4614 n = 730
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0003124102,0.0000216249]
## (score 1256.521 & scale 1.584753).
## Hessian positive definite, eigenvalue range [0.4198283,366.8126].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.22 0.91 <0.0000000000000002 ***
## s(Year_fac) 27.00 18.27 NA NA
## s(Station_fac) 3.00 1.33 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4095 0.1191 11.84 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.217 19 134.252 <0.0000000000000002 ***
## s(Year_fac) 18.275 26 2.383 <0.0000000000000002 ***
## s(Station_fac) 1.335 2 1.958 0.0572 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.684 Deviance explained = 69.9%
## -REML = 1256.5 Scale est. = 1.5848 n = 734
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.000004361445,0.000002470225]
## (score 2752.772 & scale 1.827422).
## Hessian positive definite, eigenvalue range [0.6951158,779.229].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.76 0.86 <0.0000000000000002 ***
## s(Year_fac) 27.00 23.57 NA NA
## s(Station_fac) 8.00 2.93 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8923 0.1272 14.88 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.756 19 495.454 <0.0000000000000002 ***
## s(Year_fac) 23.570 26 9.697 <0.0000000000000002 ***
## s(Station_fac) 2.926 7 0.858 0.0646 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.678 Deviance explained = 68.7%
## -REML = 2752.8 Scale est. = 1.8274 n = 1559
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.002354924,0.01221993]
## (score 3178.883 & scale 2.562011).
## Hessian positive definite, eigenvalue range [0.006762034,822.6947].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 15.630 0.87 <0.0000000000000002 ***
## s(Year_fac) 27.000 23.468 NA NA
## s(Station_fac) 10.000 0.236 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.4087 0.1353 17.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.6304 19 307.590 <0.0000000000000002 ***
## s(Year_fac) 23.4682 26 10.296 <0.0000000000000002 ***
## s(Station_fac) 0.2358 9 0.027 0.399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.572 Deviance explained = 58.2%
## -REML = 3178.9 Scale est. = 2.562 n = 1646
## <<<<<<<<<<<<<<<<<<<<<<< modeling daphnia Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.000008894614,0.000005076387]
## (score 2408.63 & scale 2.245173).
## Hessian positive definite, eigenvalue range [1.033501,646.2134].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 12.98 0.94 <0.0000000000000002 ***
## s(Year_fac) 27.00 21.10 NA NA
## s(Station_fac) 6.00 3.97 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3285 0.1769 7.509 0.000000000000113 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 12.98 19 61.556 < 0.0000000000000002 ***
## s(Year_fac) 21.10 26 4.468 < 0.0000000000000002 ***
## s(Station_fac) 3.97 5 6.081 0.0000035 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.463 Deviance explained = 47.9%
## -REML = 2408.6 Scale est. = 2.2452 n = 1293
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0007049716,0.0005200557]
## (score 1458.787 & scale 2.748531).
## Hessian positive definite, eigenvalue range [1.337259,369.1539].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 12.74 0.98 0.22
## s(Year_fac) 27.00 11.25 NA NA
## s(Station_fac) 5.00 3.57 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1345 0.6754 4.641 0.00000413 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 12.739 19 1486.159 < 0.0000000000000002 ***
## s(Year_fac) 11.254 26 0.792 0.00896 **
## s(Station_fac) 3.573 4 8.186 0.00000136 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.633 Deviance explained = 64.6%
## -REML = 1458.8 Scale est. = 2.7485 n = 739
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.001070905,0.0008738872]
## (score 1529.328 & scale 2.639282).
## Hessian positive definite, eigenvalue range [0.06028248,388.3144].
## Model rank = 51 / 51
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 15.710 0.97 0.19
## s(Year_fac) 27.000 18.891 NA NA
## s(Station_fac) 4.000 0.493 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.5979 0.1231 21.11 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.7097 19 84.461 <0.0000000000000002 ***
## s(Year_fac) 18.8907 26 2.696 <0.0000000000000002 ***
## s(Station_fac) 0.4927 3 0.218 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.598 Deviance explained = 61.6%
## -REML = 1529.3 Scale est. = 2.6393 n = 777
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.000001429611,0.00000003249666]
## (score 3088.542 & scale 2.498972).
## Hessian positive definite, eigenvalue range [1.184303,803.6996].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 17.46 0.79 <0.0000000000000002 ***
## s(Year_fac) 27.00 21.37 NA NA
## s(Station_fac) 8.00 5.21 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9254 0.1411 20.73 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 17.462 19 257.528 < 0.0000000000000002 ***
## s(Year_fac) 21.373 26 4.162 < 0.0000000000000002 ***
## s(Station_fac) 5.206 7 3.329 0.000195 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.631 Deviance explained = 64.1%
## -REML = 3088.5 Scale est. = 2.499 n = 1608
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 12 iterations.
## Gradient range [-0.0000314416,0.00002883043]
## (score 3211.96 & scale 2.644034).
## Hessian positive definite, eigenvalue range [1.504962,823.1962].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.95 0.74 <0.0000000000000002 ***
## s(Year_fac) 27.00 21.71 NA NA
## s(Station_fac) 10.00 4.98 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9864 0.1313 22.75 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.95 19 195.351 < 0.0000000000000002 ***
## s(Year_fac) 21.71 26 5.436 < 0.0000000000000002 ***
## s(Station_fac) 4.98 9 2.267 0.000349 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.615 Deviance explained = 62.5%
## -REML = 3212 Scale est. = 2.644 n = 1647
## <<<<<<<<<<<<<<<<<<<<<<< modeling eurytem Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0003350443,0.0002726983]
## (score 2813.07 & scale 2.741279).
## Hessian positive definite, eigenvalue range [1.229002,714.7294].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.04 0.76 <0.0000000000000002 ***
## s(Year_fac) 27.00 22.91 NA NA
## s(Station_fac) 8.00 4.47 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1578 0.1974 21.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.037 19 202.677 <0.0000000000000002 ***
## s(Year_fac) 22.913 26 6.577 <0.0000000000000002 ***
## s(Station_fac) 4.474 7 8.104 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.647 Deviance explained = 65.8%
## -REML = 2813.1 Scale est. = 2.7413 n = 1430
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0007416999,0.00005038202]
## (score 1653.883 & scale 2.559664).
## Hessian positive definite, eigenvalue range [1.242421,427.2633].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 11.40 1 0.41
## s(Year_fac) 27.00 19.22 NA NA
## s(Station_fac) 5.00 2.86 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.6466 0.3377 19.68 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 11.401 19 100.352 <0.0000000000000002 ***
## s(Year_fac) 19.221 26 3.431 <0.0000000000000002 ***
## s(Station_fac) 2.857 4 27.281 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.386 Deviance explained = 41.1%
## -REML = 1653.9 Scale est. = 2.5597 n = 855
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.001038173,0.0008311656]
## (score 1827.875 & scale 1.831343).
## Hessian positive definite, eigenvalue range [0.736008,520.5772].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.00 0.95 0.025 *
## s(Year_fac) 27.00 6.16 NA NA
## s(Station_fac) 5.00 2.72 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1337 0.1717 35.73 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.003 19 60.643 <0.0000000000000002 ***
## s(Year_fac) 6.163 26 0.318 0.125
## s(Station_fac) 2.720 4 11.818 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.417 Deviance explained = 43.1%
## -REML = 1827.9 Scale est. = 1.8313 n = 1042
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.00002579016,0.000006583897]
## (score 3687.371 & scale 2.076338).
## Hessian positive definite, eigenvalue range [1.894969,1017.642].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.63 0.91 <0.0000000000000002 ***
## s(Year_fac) 27.00 21.08 NA NA
## s(Station_fac) 10.00 5.87 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5027 0.1252 43.96 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.627 19 85.258 <0.0000000000000002 ***
## s(Year_fac) 21.081 26 4.337 <0.0000000000000002 ***
## s(Station_fac) 5.873 9 6.284 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.303 Deviance explained = 31.7%
## -REML = 3687.4 Scale est. = 2.0763 n = 2036
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.001984523,0.001692732]
## (score 3615.176 & scale 2.561037).
## Hessian positive definite, eigenvalue range [2.353224,933.2142].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.19 0.88 <0.0000000000000002 ***
## s(Year_fac) 27.00 24.56 NA NA
## s(Station_fac) 10.00 6.97 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7759 0.2237 21.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.188 19 461.945 <0.0000000000000002 ***
## s(Year_fac) 24.558 26 16.746 <0.0000000000000002 ***
## s(Station_fac) 6.972 9 8.835 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.552 Deviance explained = 56.4%
## -REML = 3615.2 Scale est. = 2.561 n = 1867
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcalad Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 11 iterations.
## Gradient range [-0.002103959,0.001942017]
## (score 3102.295 & scale 2.34455).
## Hessian positive definite, eigenvalue range [2.12235,820.7098].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.67 0.93 <0.0000000000000002 ***
## s(Year_fac) 27.00 22.33 NA NA
## s(Station_fac) 8.00 5.94 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6610 0.2296 24.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.667 19 365.313 <0.0000000000000002 ***
## s(Year_fac) 22.331 26 6.875 <0.0000000000000002 ***
## s(Station_fac) 5.944 7 14.078 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.492 Deviance explained = 50.6%
## -REML = 3102.3 Scale est. = 2.3445 n = 1642
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.00003002771,0.00002698134]
## (score 1347.676 & scale 1.182251).
## Hessian positive definite, eigenvalue range [1.033518,435.6758].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 12.88 1.03 0.79
## s(Year_fac) 27.00 14.48 NA NA
## s(Station_fac) 5.00 3.39 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.0229 0.3565 19.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 12.881 19 44.567 0.00172 **
## s(Year_fac) 14.475 26 1.204 0.00101 **
## s(Station_fac) 3.394 4 6.872 0.0000186 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.336 Deviance explained = 36%
## -REML = 1347.7 Scale est. = 1.1823 n = 872
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.00002204541,0.00001666173]
## (score 1546.635 & scale 0.9996195).
## Hessian positive definite, eigenvalue range [0.1446414,526.2809].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 15.201 0.95 0.015 *
## s(Year_fac) 27.000 21.606 NA NA
## s(Station_fac) 5.000 0.928 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.49101 0.08527 76.13 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.2014 19 68.636 <0.0000000000000002 ***
## s(Year_fac) 21.6059 26 4.697 <0.0000000000000002 ***
## s(Station_fac) 0.9284 4 0.384 0.195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.402 Deviance explained = 42.4%
## -REML = 1546.6 Scale est. = 0.99962 n = 1053
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.0007353555,0.0002111834]
## (score 3113.504 & scale 1.009186).
## Hessian positive definite, eigenvalue range [2.184889,1067.651].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.86 0.82 <0.0000000000000002 ***
## s(Year_fac) 27.00 21.08 NA NA
## s(Station_fac) 10.00 7.07 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.6135 0.1213 54.53 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.86 19 247.727 <0.0000000000000002 ***
## s(Year_fac) 21.08 26 5.007 <0.0000000000000002 ***
## s(Station_fac) 7.07 9 8.499 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.457 Deviance explained = 46.8%
## -REML = 3113.5 Scale est. = 1.0092 n = 2136
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 13 iterations.
## Gradient range [-0.00002334144,0.000002622975]
## (score 2856.866 & scale 0.8144793).
## Hessian positive definite, eigenvalue range [2.782338,1054.664].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.77 0.77 <0.0000000000000002 ***
## s(Year_fac) 27.00 22.18 NA NA
## s(Station_fac) 10.00 7.26 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.7914 0.1025 66.24 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.771 19 842.088 <0.0000000000000002 ***
## s(Year_fac) 22.185 26 6.579 <0.0000000000000002 ***
## s(Station_fac) 7.255 9 14.314 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.662 Deviance explained = 66.9%
## -REML = 2856.9 Scale est. = 0.81448 n = 2110
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcaljuv Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.001746193,0.001381503]
## (score 2504.624 & scale 0.9664801).
## Hessian positive definite, eigenvalue range [2.16893,868.1696].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0 16.7 0.9 <0.0000000000000002 ***
## s(Year_fac) 27.0 19.9 NA NA
## s(Station_fac) 8.0 6.6 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.7287 0.1997 33.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.709 19 198.963 <0.0000000000000002 ***
## s(Year_fac) 19.881 26 3.529 <0.0000000000000002 ***
## s(Station_fac) 6.597 7 50.529 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.413 Deviance explained = 42.7%
## -REML = 2504.6 Scale est. = 0.96648 n = 1737
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.000008705367,0.000005907735]
## (score 1181.15 & scale 1.244969).
## Hessian positive definite, eigenvalue range [0.2077835,372.8422].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 10.189 1 0.52
## s(Year_fac) 27.000 21.137 NA NA
## s(Station_fac) 5.000 0.978 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.293 0.109 11.87 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 10.1895 19 202.043 <0.0000000000000002 ***
## s(Year_fac) 21.1368 26 4.029 <0.0000000000000002 ***
## s(Station_fac) 0.9781 4 0.486 0.136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.703 Deviance explained = 71.6%
## -REML = 1181.1 Scale est. = 1.245 n = 746
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0004317457,0.0002782451]
## (score 1225.934 & scale 1.23228).
## Hessian positive definite, eigenvalue range [0.0004316071,387.3476].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00000 12.33429 0.89 <0.0000000000000002 ***
## s(Year_fac) 27.00000 21.20347 NA NA
## s(Station_fac) 5.00000 0.00142 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.69186 0.09834 17.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 12.334291 19 298.538 <0.0000000000000002 ***
## s(Year_fac) 21.203470 26 4.282 <0.0000000000000002 ***
## s(Station_fac) 0.001421 4 0.000 0.678
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.76 Deviance explained = 77%
## -REML = 1225.9 Scale est. = 1.2323 n = 775
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.000005516281,0.000004757904]
## (score 2701.618 & scale 1.246219).
## Hessian positive definite, eigenvalue range [1.413185,857.7241].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.06 0.85 <0.0000000000000002 ***
## s(Year_fac) 27.00 24.40 NA NA
## s(Station_fac) 9.00 5.11 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.235 0.134 16.68 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.061 19 1497.561 < 0.0000000000000002 ***
## s(Year_fac) 24.404 26 15.419 < 0.0000000000000002 ***
## s(Station_fac) 5.115 8 3.267 0.0000663 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.766 Deviance explained = 77.2%
## -REML = 2701.6 Scale est. = 1.2462 n = 1716
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0005140318,0.00009486559]
## (score 3170.835 & scale 1.410279).
## Hessian positive definite, eigenvalue range [1.53027,972.1918].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.18 0.89 <0.0000000000000002 ***
## s(Year_fac) 27.00 24.33 NA NA
## s(Station_fac) 10.00 7.21 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1273 0.1639 19.07 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.182 19 1459.28 <0.0000000000000002 ***
## s(Year_fac) 24.327 26 16.05 <0.0000000000000002 ***
## s(Station_fac) 7.213 9 19.11 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.684 Deviance explained = 69.2%
## -REML = 3170.8 Scale est. = 1.4103 n = 1945
## <<<<<<<<<<<<<<<<<<<<<<< modeling othclad Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.000001468536,0.0000006484035]
## (score 2423.487 & scale 1.490508).
## Hessian positive definite, eigenvalue range [1.380582,728.7322].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 12.86 0.97 0.11
## s(Year_fac) 27.00 23.55 NA NA
## s(Station_fac) 8.00 5.33 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7017 0.1933 8.802 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 12.864 19 396.18 <0.0000000000000002 ***
## s(Year_fac) 23.553 26 10.34 <0.0000000000000002 ***
## s(Station_fac) 5.334 7 19.02 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.553 Deviance explained = 56.6%
## -REML = 2423.5 Scale est. = 1.4905 n = 1458
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.001468335,0.00130738]
## (score 1591.175 & scale 2.268222).
## Hessian positive definite, eigenvalue range [0.7085017,420.8405].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.02 0.98 0.22
## s(Year_fac) 27.00 21.52 NA NA
## s(Station_fac) 5.00 3.14 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2489 0.4068 7.987 0.0000000000000048 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.020 19 772.256 <0.0000000000000002 ***
## s(Year_fac) 21.515 26 4.413 <0.0000000000000002 ***
## s(Station_fac) 3.143 4 3.283 0.0139 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.612 Deviance explained = 62.9%
## -REML = 1591.2 Scale est. = 2.2682 n = 842
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.0005166779,0.0004110887]
## (score 1831.367 & scale 1.96025).
## Hessian positive definite, eigenvalue range [0.0001786789,507.7936].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 10.662654 0.94 0.025 *
## s(Year_fac) 27.000000 22.545327 NA NA
## s(Station_fac) 5.000000 0.000402 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4691 0.1292 26.86 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 10.662654 19 191.352 <0.0000000000000002 ***
## s(Year_fac) 22.545327 26 5.744 <0.0000000000000002 ***
## s(Station_fac) 0.000402 4 0.000 0.936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.641 Deviance explained = 65.3%
## -REML = 1831.4 Scale est. = 1.9602 n = 1016
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.00148126,0.001442375]
## (score 3878.283 & scale 2.177848).
## Hessian positive definite, eigenvalue range [1.630563,1052.671].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.84 0.79 <0.0000000000000002 ***
## s(Year_fac) 27.00 23.63 NA NA
## s(Station_fac) 10.00 6.67 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3298 0.1759 24.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.845 19 905.573 <0.0000000000000002 ***
## s(Year_fac) 23.625 26 10.175 <0.0000000000000002 ***
## s(Station_fac) 6.666 9 9.135 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.681 Deviance explained = 68.7%
## -REML = 3878.3 Scale est. = 2.1778 n = 2106
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 12 iterations.
## Gradient range [-0.00001593149,0.00001504367]
## (score 3902.761 & scale 2.222024).
## Hessian positive definite, eigenvalue range [1.624398,1053.169].
## Model rank = 57 / 57
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.06 0.7 <0.0000000000000002 ***
## s(Year_fac) 27.00 23.64 NA NA
## s(Station_fac) 10.00 6.51 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.976 0.153 39.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.06 19 521.726 <0.0000000000000002 ***
## s(Year_fac) 23.64 26 10.286 <0.0000000000000002 ***
## s(Station_fac) 6.51 9 6.915 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.706 Deviance explained = 71.2%
## -REML = 3902.8 Scale est. = 2.222 n = 2107
## <<<<<<<<<<<<<<<<<<<<<<< modeling pdiapfor Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.004182847,0.003469361]
## (score 3130.111 & scale 1.962958).
## Hessian positive definite, eigenvalue range [1.306199,865.7348].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.31 0.89 <0.0000000000000002 ***
## s(Year_fac) 27.00 24.77 NA NA
## s(Station_fac) 8.00 6.27 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.278 0.263 20.07 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.310 19 2123.91 <0.0000000000000002 ***
## s(Year_fac) 24.771 26 18.37 <0.0000000000000002 ***
## s(Station_fac) 6.274 7 26.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.704 Deviance explained = 71.2%
## -REML = 3130.1 Scale est. = 1.963 n = 1732
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.000154612,0.00007258112]
## (score 601.3534 & scale 2.330002).
## Hessian positive definite, eigenvalue range [0.0001545943,158.25].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 7.843525 1.19 1
## s(Year_fac) 27.000000 11.179917 NA NA
## s(Station_fac) 3.000000 0.000455 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1026 0.1156 9.541 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 7.8435253 19 10.457 < 0.0000000000000002 ***
## s(Year_fac) 11.1799165 26 0.784 0.00904 **
## s(Station_fac) 0.0004546 2 0.000 0.69048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.372 Deviance explained = 41%
## -REML = 601.35 Scale est. = 2.33 n = 317
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0000001619108,0.000000007356861]
## (score 617.8933 & scale 2.599724).
## Hessian positive definite, eigenvalue range [1.238011,155.1693].
## Model rank = 47 / 47
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 9.56 0.99 0.42
## s(Year_fac) 27.00 18.80 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4834 0.1786 8.307 0.00000000000000423 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 9.562 19 13.277 <0.0000000000000002 ***
## s(Year_fac) 18.797 26 2.567 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.443 Deviance explained = 49.4%
## -REML = 617.89 Scale est. = 2.5997 n = 310
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0003086585,0.0002553844]
## (score 1262.904 & scale 2.221373).
## Hessian positive definite, eigenvalue range [0.4256681,334.4252].
## Model rank = 51 / 51
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 12.91 0.91 <0.0000000000000002 ***
## s(Year_fac) 27.00 21.63 NA NA
## s(Station_fac) 4.00 1.84 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7264 0.2072 8.334 0.000000000000000489 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 12.909 19 49.826 <0.0000000000000002 ***
## s(Year_fac) 21.634 26 5.032 <0.0000000000000002 ***
## s(Station_fac) 1.841 3 2.151 0.0305 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.536 Deviance explained = 56.1%
## -REML = 1262.9 Scale est. = 2.2214 n = 669
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.0004303728,0.00045683]
## (score 1345.29 & scale 2.474344).
## Hessian positive definite, eigenvalue range [0.1057984,347.3914].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 12.154 0.89 0.01 **
## s(Year_fac) 27.000 21.371 NA NA
## s(Station_fac) 5.000 0.522 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7959 0.1531 11.73 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 12.1538 19 29.248 <0.0000000000000002 ***
## s(Year_fac) 21.3712 26 4.793 <0.0000000000000002 ***
## s(Station_fac) 0.5224 4 0.207 0.197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.468 Deviance explained = 49.4%
## -REML = 1345.3 Scale est. = 2.4743 n = 695
## <<<<<<<<<<<<<<<<<<<<<<< modeling allcopnaup Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.0002834877,0.0000007071801]
## (score 1324.81 & scale 3.654516).
## Hessian positive definite, eigenvalue range [0.0002833269,310.4633].
## Model rank = 49 / 49
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 9.462557 0.89 <0.0000000000000002 ***
## s(Year_fac) 27.000000 22.659249 NA NA
## s(Station_fac) 2.000000 0.000568 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7120 0.2207 7.758 0.0000000000000383 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 9.4625568 19 17.208 <0.0000000000000002 ***
## s(Year_fac) 22.6592491 26 6.757 <0.0000000000000002 ***
## s(Station_fac) 0.0005679 1 0.000 0.967
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.399 Deviance explained = 43%
## -REML = 1324.8 Scale est. = 3.6545 n = 621
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 5 iterations.
## Gradient range [-0.00005651404,0.00005859625]
## (score 681.6302 & scale 3.422221).
## Hessian positive definite, eigenvalue range [0.00004508318,161.4539].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 9.333769 0.88 0.005 **
## s(Year_fac) 27.000000 15.551463 NA NA
## s(Station_fac) 3.000000 0.000172 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6536 0.1665 33.96 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 9.3337692 19 7.302 < 0.0000000000000002 ***
## s(Year_fac) 15.5514628 26 1.494 0.000107 ***
## s(Station_fac) 0.0001718 2 0.000 0.583339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.343 Deviance explained = 39.4%
## -REML = 681.63 Scale est. = 3.4222 n = 323
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.000150143,0.00009574354]
## (score 600.4573 & scale 2.201123).
## Hessian positive definite, eigenvalue range [1.593401,156.5484].
## Model rank = 47 / 47
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0 11.7 0.98 0.29
## s(Year_fac) 27.0 16.2 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.4074 0.1397 45.85 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 11.75 19 46.76 < 0.0000000000000002 ***
## s(Year_fac) 16.17 26 1.67 0.0000324 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.703 Deviance explained = 73%
## -REML = 600.46 Scale est. = 2.2011 n = 313
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 10 iterations.
## Gradient range [-0.000000239289,0.0000001464564]
## (score 1236.472 & scale 1.965841).
## Hessian positive definite, eigenvalue range [0.8025186,335.9462].
## Model rank = 51 / 51
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.16 0.85 <0.0000000000000002 ***
## s(Year_fac) 27.00 21.43 NA NA
## s(Station_fac) 4.00 2.32 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.765 0.245 27.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.158 19 123.851 < 0.0000000000000002 ***
## s(Year_fac) 21.432 26 4.426 < 0.0000000000000002 ***
## s(Station_fac) 2.319 3 5.541 0.000576 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.703 Deviance explained = 72%
## -REML = 1236.5 Scale est. = 1.9658 n = 672
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.00004883231,0.000008133469]
## (score 1345.344 & scale 2.325183).
## Hessian positive definite, eigenvalue range [1.009805,350.4133].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.78 0.95 0.11
## s(Year_fac) 27.00 21.16 NA NA
## s(Station_fac) 5.00 2.34 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.4572 0.2244 24.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.784 19 187.879 < 0.0000000000000002 ***
## s(Year_fac) 21.155 26 4.447 < 0.0000000000000002 ***
## s(Station_fac) 2.337 4 5.369 0.0000308 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.748 Deviance explained = 76.2%
## -REML = 1345.3 Scale est. = 2.3252 n = 701
## <<<<<<<<<<<<<<<<<<<<<<< modeling limno Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.000001770403,0.000001619472]
## (score 1260.551 & scale 2.853194).
## Hessian positive definite, eigenvalue range [0.472618,313.8114].
## Model rank = 49 / 49
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 13.889 0.96 0.15
## s(Year_fac) 27.000 16.907 NA NA
## s(Station_fac) 2.000 0.973 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8838 0.4269 13.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.8895 19 92.212 < 0.0000000000000002 ***
## s(Year_fac) 16.9071 26 1.801 0.00000818 ***
## s(Station_fac) 0.9731 1 36.476 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.693 Deviance explained = 70.8%
## -REML = 1260.6 Scale est. = 2.8532 n = 628
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0000002316543,0.0000002354506]
## (score 788.2818 & scale 4.303684).
## Hessian positive definite, eigenvalue range [0.009941399,175.6295].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000 9.974 1.02 0.66
## s(Year_fac) 26.000 19.379 NA NA
## s(Station_fac) 4.000 0.206 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4872 0.2719 16.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 9.9737 19 25.475 <0.0000000000000002 ***
## s(Year_fac) 19.3786 25 3.391 <0.0000000000000002 ***
## s(Station_fac) 0.2062 3 0.087 0.283
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.553 Deviance explained = 59.1%
## -REML = 788.28 Scale est. = 4.3037 n = 351
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.000001090259,0.000001039541]
## (score 892.3173 & scale 3.728494).
## Hessian positive definite, eigenvalue range [0.412642,206.4459].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 8.49 0.91 0.015 *
## s(Year_fac) 26.00 17.72 NA NA
## s(Station_fac) 4.00 2.94 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.582 1.104 4.15 0.0000411 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 8.490 19 13.03 0.0032 **
## s(Year_fac) 17.721 25 2.67 <0.0000000000000002 ***
## s(Station_fac) 2.943 3 103.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.506 Deviance explained = 54.1%
## -REML = 892.32 Scale est. = 3.7285 n = 413
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 5 iterations.
## Gradient range [-0.0007557609,0.0004340541]
## (score 1764.733 & scale 3.552136).
## Hessian positive definite, eigenvalue range [2.033551,414.8406].
## Model rank = 56 / 56
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.83 0.9 <0.0000000000000002 ***
## s(Year_fac) 26.00 19.48 NA NA
## s(Station_fac) 10.00 8.08 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.316 0.483 11.01 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.829 19 66.943 <0.0000000000000002 ***
## s(Year_fac) 19.484 25 4.362 <0.0000000000000002 ***
## s(Station_fac) 8.081 9 15.525 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.536 Deviance explained = 56%
## -REML = 1764.7 Scale est. = 3.5521 n = 830
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 5 iterations.
## Gradient range [-0.0005129698,0.0003546754]
## (score 1640.225 & scale 3.123566).
## Hessian positive definite, eigenvalue range [2.162904,396.8742].
## Model rank = 55 / 55
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.10 0.92 <0.0000000000000002 ***
## s(Year_fac) 26.00 20.63 NA NA
## s(Station_fac) 9.00 6.14 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2625 0.3185 13.38 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.103 19 99.083 <0.0000000000000002 ***
## s(Year_fac) 20.632 25 4.998 <0.0000000000000002 ***
## s(Station_fac) 6.138 8 9.453 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.57 Deviance explained = 59.3%
## -REML = 1640.2 Scale est. = 3.1236 n = 794
## <<<<<<<<<<<<<<<<<<<<<<< modeling mysid Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0000002627639,0.0000001877132]
## (score 1470.029 & scale 3.254926).
## Hessian positive definite, eigenvalue range [1.808892,349.4785].
## Model rank = 53 / 53
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.14 0.93 0.005 **
## s(Year_fac) 26.00 22.55 NA NA
## s(Station_fac) 7.00 5.43 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.3757 0.6341 8.478 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.136 19 71.675 <0.0000000000000002 ***
## s(Year_fac) 22.547 25 8.985 <0.0000000000000002 ***
## s(Station_fac) 5.434 6 16.946 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.493 Deviance explained = 52.4%
## -REML = 1470 Scale est. = 3.2549 n = 699
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0000002064242,0.0000001313375]
## (score 631.4934 & scale 2.689229).
## Hessian positive definite, eigenvalue range [0.3016207,161.1797].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 4.74 1.05 0.68
## s(Year_fac) 27.00 10.04 NA NA
## s(Station_fac) 3.00 1.35 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.2242 0.6539 12.58 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 4.741 19 2.071 0.0000021 ***
## s(Year_fac) 10.044 26 0.633 0.0285 *
## s(Station_fac) 1.354 2 2.475 0.0343 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.154 Deviance explained = 19.6%
## -REML = 631.49 Scale est. = 2.6892 n = 323
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0000003166628,0.00000001099926]
## (score 535.4435 & scale 1.422309).
## Hessian positive definite, eigenvalue range [1.242915,156.7068].
## Model rank = 47 / 47
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.0 12.2 0.96 0.2
## s(Year_fac) 27.0 18.8 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.5893 0.1317 72.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 12.18 19 31.192 <0.0000000000000002 ***
## s(Year_fac) 18.76 26 2.619 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.601 Deviance explained = 64%
## -REML = 535.44 Scale est. = 1.4223 n = 313
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.00002785172,0.000004633361]
## (score 915.9835 & scale 0.7496992).
## Hessian positive definite, eigenvalue range [0.561049,335.9529].
## Model rank = 51 / 51
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 16.17 0.9 0.005 **
## s(Year_fac) 27.00 21.26 NA NA
## s(Station_fac) 4.00 1.77 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.2526 0.1153 80.28 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 16.168 19 189.288 < 0.0000000000000002 ***
## s(Year_fac) 21.260 26 4.573 < 0.0000000000000002 ***
## s(Station_fac) 1.766 3 3.353 0.00306 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.753 Deviance explained = 76.7%
## -REML = 915.98 Scale est. = 0.7497 n = 672
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.000000512982,0.0000002724996]
## (score 1278.834 & scale 1.970803).
## Hessian positive definite, eigenvalue range [0.526458,350.3066].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 15.02 0.98 0.35
## s(Year_fac) 27.00 17.46 NA NA
## s(Station_fac) 5.00 1.63 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.2865 0.1381 59.99 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 15.03 19 84.263 < 0.0000000000000002 ***
## s(Year_fac) 17.46 26 1.997 0.00000189 ***
## s(Station_fac) 1.63 4 1.564 0.0166 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.624 Deviance explained = 64.3%
## -REML = 1278.8 Scale est. = 1.9708 n = 701
## <<<<<<<<<<<<<<<<<<<<<<< modeling othcyc Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0002389409,0.0001754298]
## (score 998.6822 & scale 1.23984).
## Hessian positive definite, eigenvalue range [0.4798591,313.7857].
## Model rank = 49 / 49
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 14.16 0.96 0.23
## s(Year_fac) 27.00 15.83 NA NA
## s(Station_fac) 2.00 0.98 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.3379 0.3261 28.64 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.1618 19 59.719 < 0.0000000000000002 ***
## s(Year_fac) 15.8272 26 1.537 0.0000547 ***
## s(Station_fac) 0.9804 1 50.302 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.599 Deviance explained = 61.9%
## -REML = 998.68 Scale est. = 1.2398 n = 628
## <<<<<<<<<<<<<<<<<<<<<<< modeling other SW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0001181306,0.00003164694]
## (score 657.8992 & scale 3.184574).
## Hessian positive definite, eigenvalue range [0.0001180994,158.5305].
## Model rank = 50 / 50
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.000000 7.106973 0.89 0.02 *
## s(Year_fac) 27.000000 17.271773 NA NA
## s(Station_fac) 3.000000 0.000388 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.8324 0.1769 38.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 7.1069729 19 2.572 0.000245 ***
## s(Year_fac) 17.2717731 26 2.036 0.00000223 ***
## s(Station_fac) 0.0003876 2 0.000 0.637914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.23 Deviance explained = 28.9%
## -REML = 657.9 Scale est. = 3.1846 n = 317
## <<<<<<<<<<<<<<<<<<<<<<< modeling other NW Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0005107396,0.0001649552]
## (score 681.1145 & scale 4.221794).
## Hessian positive definite, eigenvalue range [0.3661494,154.8462].
## Model rank = 47 / 47
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 6.33 0.96 0.18
## s(Year_fac) 27.00 13.74 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.0950 0.1717 35.49 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 6.328 19 3.101 0.000000538 ***
## s(Year_fac) 13.735 26 1.133 0.00125 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.22 Deviance explained = 27%
## -REML = 681.11 Scale est. = 4.2218 n = 310
## <<<<<<<<<<<<<<<<<<<<<<< modeling other East Suisun >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0009663949,0.0005020085]
## (score 1416.959 & scale 3.606287).
## Hessian positive definite, eigenvalue range [0.3912403,334.3088].
## Model rank = 51 / 51
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00 13.45 0.84 <0.0000000000000002 ***
## s(Year_fac) 27.00 17.70 NA NA
## s(Station_fac) 4.00 1.53 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.9111 0.1991 29.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 13.455 19 11.270 < 0.0000000000000002 ***
## s(Year_fac) 17.696 26 1.993 0.00000251 ***
## s(Station_fac) 1.529 3 1.954 0.0193 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.277 Deviance explained = 31.2%
## -REML = 1417 Scale est. = 3.6063 n = 669
## <<<<<<<<<<<<<<<<<<<<<<< modeling other Confluence >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 9 iterations.
## Gradient range [-0.0005075893,0.0001711053]
## (score 1439.908 & scale 3.172422).
## Hessian positive definite, eigenvalue range [0.0005072981,347.4243].
## Model rank = 52 / 52
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00000 14.77973 0.9 0.005 **
## s(Year_fac) 27.00000 21.63427 NA NA
## s(Station_fac) 5.00000 0.00136 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5934 0.1717 32.57 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 14.779732 19 19.038 <0.0000000000000002 ***
## s(Year_fac) 21.634272 26 4.142 <0.0000000000000002 ***
## s(Station_fac) 0.001358 4 0.000 0.789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.383 Deviance explained = 41.5%
## -REML = 1439.9 Scale est. = 3.1724 n = 695
## <<<<<<<<<<<<<<<<<<<<<<< modeling other Suisun Marsh >>>>>>>>>>>>>>>>>>>>>>>>>
##
## -------------gam check-------------
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0005359542,0.0001920192]
## (score 1360.445 & scale 4.14572).
## Hessian positive definite, eigenvalue range [0.0005353824,310.394].
## Model rank = 49 / 49
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## te(SalSurf_s,doy_s) 19.00000 11.31243 0.98 0.26
## s(Year_fac) 27.00000 20.32838 NA NA
## s(Station_fac) 2.00000 0.00115 NA NA
##
##
## -------------summary-------------
##
## Family: gaussian
## Link function: identity
##
## Formula:
## BPUE_log1p ~ te(SalSurf_s, doy_s, k = c(5, 5), bs = c("cs", "cc")) +
## s(Year_fac, bs = "re") + s(Station_fac, bs = "re")
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6270 0.1803 31.21 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## te(SalSurf_s,doy_s) 11.312432 19 12.325 <0.0000000000000002 ***
## s(Year_fac) 20.328382 26 3.602 <0.0000000000000002 ***
## s(Station_fac) 0.001147 1 0.000 0.797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.354 Deviance explained = 38.7%
## -REML = 1360.4 Scale est. = 4.1457 n = 621sal_conversions## # A tibble: 88,104 × 1,004
## Region Month IBMR SalSurf draw_1 draw_2 draw_3 draw_4 draw_5 draw_6 draw_7
## <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SW Suis… 1 acar… 0.1 5.16 3.70 5.69 5.14 3.92 3.87 3.95
## 2 SW Suis… 1 acar… 0.2 5.16 3.71 5.70 5.15 3.95 3.89 3.95
## 3 SW Suis… 1 acar… 0.3 5.15 3.72 5.71 5.15 3.97 3.91 3.95
## 4 SW Suis… 1 acar… 0.4 5.14 3.72 5.72 5.16 4.00 3.92 3.95
## 5 SW Suis… 1 acar… 0.5 5.14 3.73 5.73 5.16 4.03 3.94 3.96
## 6 SW Suis… 1 acar… 0.6 5.13 3.73 5.74 5.17 4.06 3.96 3.96
## 7 SW Suis… 1 acar… 0.7 5.12 3.74 5.75 5.17 4.08 3.97 3.96
## 8 SW Suis… 1 acar… 0.8 5.12 3.74 5.75 5.18 4.11 3.99 3.96
## 9 SW Suis… 1 acar… 0.9 5.11 3.75 5.76 5.18 4.14 4.01 3.96
## 10 SW Suis… 1 acar… 1 5.10 3.76 5.77 5.19 4.16 4.02 3.96
## # … with 88,094 more rows, and 993 more variables: draw_8 <dbl>, draw_9 <dbl>,
## # draw_10 <dbl>, draw_11 <dbl>, draw_12 <dbl>, draw_13 <dbl>, draw_14 <dbl>,
## # draw_15 <dbl>, draw_16 <dbl>, draw_17 <dbl>, draw_18 <dbl>, draw_19 <dbl>,
## # draw_20 <dbl>, draw_21 <dbl>, draw_22 <dbl>, draw_23 <dbl>, draw_24 <dbl>,
## # draw_25 <dbl>, draw_26 <dbl>, draw_27 <dbl>, draw_28 <dbl>, draw_29 <dbl>,
## # draw_30 <dbl>, draw_31 <dbl>, draw_32 <dbl>, draw_33 <dbl>, draw_34 <dbl>,
## # draw_35 <dbl>, draw_36 <dbl>, draw_37 <dbl>, draw_38 <dbl>, …Plot salinity-biomass relationships
sal_conversions_sum<-apply(select(sal_conversions, starts_with("draw_")), 1,
function(x) quantile(x, c(0.025, 0.5, 0.975)))
sal_conversions_plot<-sal_conversions%>%
select(-starts_with("draw_"))%>%
bind_cols(tibble(l95=sal_conversions_sum["2.5%",],
median=sal_conversions_sum["50%",],
u95=sal_conversions_sum["97.5%",]))
plot_sal_conversions<-function(group, data=sal_conversions_plot){
if(group!="All"){
data<-filter(data, IBMR%in%group)
ggplot(data, aes(x=SalSurf, y=median, ymin=l95, ymax=u95))+
geom_ribbon(alpha=0.4, fill="chartreuse4")+
ylab("Zooplankton biomass (log scale)")+
facet_grid(Region~month(Month, label=T))+
theme_bw()+
theme(axis.text.x=element_text(angle=45, hjust=1))
}else{
ggplot(data, aes(x=SalSurf, y=median, ymin=l95, ymax=u95, fill=IBMR))+
geom_ribbon(alpha=0.4)+
ylab("Zooplankton biomass (log scale)")+
facet_grid(Region~month(Month, label=T))+
scale_fill_viridis_d()+
theme_bw()+
theme(axis.text.x=element_text(angle=45, hjust=1))
}
}# Create plots for each IBMR group
sal_conversion_plots <- tibble(group=c("All", unique(model_factors$IBMR)))%>%
mutate(plot=map(group, plot_sal_conversions))Salinity-biomass plots
All
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
Apply model
Load in SMSCG modeled salinity
scenario_file<-"Data/CSAMP_DS_SDM_salinity_scenarios.csv"
scenario_names<-tibble(name=colnames(read.csv(scenario_file)))%>%
filter(str_detect(name, "sal_"))%>%
rev()
scenario_sal<-read_csv(scenario_file, guess_max=2800)%>%
select(region, year, month, starts_with("sal_"))%>%
mutate(across(c(year, month), as.integer),
across(starts_with("sal_"), ~if_else(is.na(.x), sal_base, .x)))%>%
filter(region%in%unique(zoop_data_mass$SUBREGION))%>%
mutate(region=factor(region,
levels=c("Confluence", "Suisun Marsh", "NE Suisun",
"SE Suisun", "NW Suisun", "SW Suisun")))%>%
pivot_longer(cols=starts_with("sal_"), names_to="Scenario", values_to="Salinity")%>% # Prepare data for easier plotting
mutate(Scenario=factor(Scenario,
levels=scenario_names$name),
Salinity=round(Salinity, 1))Plot SMSCG modeled salinity
ggplot(scenario_sal,
aes(x=year, y=Salinity, color=Scenario))+
geom_line()+
scale_color_viridis_d(direction=-1)+
facet_grid(region ~ month(month, label=T))+
theme_bw()+
theme(legend.position = "bottom", axis.text.x=element_text(angle=45, hjust=1))
Calculate zoop abundance difference between each scenario and the baseline
zoop_saladjusted<-scenario_sal%>%
mutate(Salinity=as.character(Salinity),
IBMR=unique(model_factors$IBMR)[1])%>%
complete(region, year, month, Scenario, IBMR=unique(model_factors$IBMR))%>%
group_by(region, year, month, Scenario)%>%
mutate(Salinity=na.exclude(Salinity),
region2=if_else(region%in%c("NE Suisun", "SE Suisun"), "East Suisun", as.character(region)))%>%
ungroup()%>%
left_join(sal_conversions%>%
mutate(SalSurf=as.character(SalSurf)),
by=c("region2"="Region",
"month"="Month",
"Salinity"="SalSurf",
"IBMR"="IBMR"))%>%
select(-Salinity, -region2)%>%
mutate(across(starts_with("draw_"), ~exp(.x)-1))%>%
pivot_longer(starts_with("draw_"), names_prefix="draw_", names_to="draw", values_to="fit")%>%
mutate(fit=if_else(fit<0, 0, fit))%>%
pivot_wider(names_from="Scenario", values_from="fit")%>%
mutate(across(starts_with("sal_"), ~.x/sal_base))%>%
group_by(region, year, month, IBMR)%>%
summarise(across(starts_with("sal_"),
list(median=~median(.x, na.rm=T),
l95=~quantile(.x, 0.025, na.rm=T),
u95=~quantile(.x, 0.975, na.rm=T))),
.groups="drop")
write_csv(zoop_saladjusted, file.path("Outputs", "CSAMP zoop sal adjustments.csv"))You can find the final zoop salinity adjustments here
Prepare plots
Plot the missing model results resulting from out-of-range salinity values in the inputs
missing_adjusted_data<-zoop_saladjusted%>%
select(-ends_with("l95"), -ends_with("u95"))%>%
filter(IBMR=="acartela")%>%
pivot_longer(cols=starts_with("sal_"), names_to="Scenario", values_to="zoop_change")%>%
mutate(Scenario=str_remove(Scenario, fixed("_median")))
ggplot(missing_adjusted_data,
aes(x=year, y=Scenario, fill=is.na(zoop_change)))+
geom_tile()+
scale_fill_viridis_d(name="Are the model results missing due to out-of-range salinity values?")+
facet_grid(region ~ month(month, label=T))+
theme_bw()+
theme(legend.position = "bottom", axis.text.x=element_text(angle=45, hjust=1))
Plot the result
Create some plotting functions
neglop1p<-trans_new("neglop1p", transform=function(x) sign(x)*log(abs(x)+1), inverse=function(x) sign(x)*(exp(abs(x))-1))
plot_scenario_result <- function(scenario, group) {
plot_data<-zoop_saladjusted%>%
filter(IBMR%in%group)
ggplot(plot_data,
aes(x=year, y=.data[[paste0(scenario, "_median")]], ymin=.data[[paste0(scenario, "_l95")]], ymax=.data[[paste0(scenario, "_u95")]]))+
geom_ribbon(alpha=0.4, fill="darkorchid4")+
geom_line(alpha=0.4, color="darkorchid4")+
scale_y_continuous(trans=neglop1p, breaks=c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))+
ylab("Scenario/baseline (log scale)")+
facet_grid(region ~ month(month, label=T))+
theme_bw()+
theme(legend.position = "bottom", axis.text.x=element_text(angle=45, hjust=1))
}# Create plots for each Parameter
scenario_result_plots <- expand_grid(Scenario=unique(scenario_sal$Scenario)[-1],
IBMR=unique(model_factors$IBMR))%>%
mutate(plot=map2(Scenario, IBMR, ~plot_scenario_result(.x, .y)))Result plots
sal_fallX2_hi
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_low
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_hi
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_low
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_SMSCG
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_low
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_1
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_2
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_3
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_sumX2_WAN_high
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_low
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_1
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_2
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_3
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
sal_fallX2_WAN_high
acartela
daphnia
eurytem
othcalad
othcaljuv
othclad
pdiapfor
allcopnaup
limno
mysid
othcyc
other
